Opening Notes/Comments/Announcements

  • Long Run Times for SVMs… subsample!!
  • Project Part 2 deadline?
  • ROC v Precision/Recall bonus
  • Data Usage: D,H
  • Lecture Motivation

Imports

  • the usuals
  • and some specific stuff for demonstrating gradient boosted trees
library(tidyverse)
library(ggplot2)
library(plotly)

library(rpart)
# install.packages('transformr')
library(gganimate)
library('transformr')

Toy Data Set

  • Synthetic data set we’ll use for illustrative for the lecture
# standard base R functionality
n <- 30
support_range <- 5
x <- support_range*runif(n=n)
x <- x-mean(x)
y <- x+runif(n=n)

# ALWAYS take the time to get this right for your audience!!
# http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
cbbPalette <- c("#009E73", "#CC79A7","#D55E00", "#000000",
                "#56B4E9", "#F0E442", "#0072B2", "#E69F00")

data_space_plot <- tibble(x=x,y=y) %>% 
  ggplot(aes(x=x,y=y,color='Data')) + geom_point() +
    scale_colour_manual(values=cbbPalette) 

data_space_plot + ggtitle('"Data Space"')

Gradient Descent

  • We can solve for the gradient equal to \(0\), but instead…
  • let’s see how we would numerically fit this model…
  • with Gradient Descent!

\[\LARGE \begin{align*} \sum_{i=1}^n (y_i - \hat y_i)^2 = {} & (\mathbf{y}-\mathbf{X} {\boldsymbol \beta})^T(\mathbf{y}-\mathbf{X} {\boldsymbol \beta})\\ = {} & \mathbf{y}^T\mathbf{y}-2{\boldsymbol \beta}^T \mathbf{X}^T\mathbf{y} + {\boldsymbol \beta}^T \mathbf{X}^T \mathbf{X} {\boldsymbol \beta}\\ \nabla_{\mathbf{\boldsymbol \beta}} \frac{1}{2} \left( \mathbf{y}^T\mathbf{y}-2{\boldsymbol \beta}^T \mathbf{X}^T\mathbf{y}+ {\boldsymbol \beta}^T \mathbf{X}^T \mathbf{X} {\boldsymbol \beta}\right) = {} & \frac{1}{2}(-2 \mathbf{X}^T\mathbf{y} + 2 \mathbf{X}^T \mathbf{X} {\boldsymbol \beta}) \\ = {} & \mathbf{X}^T \mathbf{X} {\boldsymbol \beta} - \mathbf{X}^T \mathbf{y}\\ = {} & \mathbf{X}^T \left(\mathbf{X} {\boldsymbol \beta} - \mathbf{y}\right) \end{align*}\]

  • PollEv.com/scottschwart658
  • What is \(\sum_{i=1}^n (y_i - \hat y_i)^2 = (\mathbf{y}-\mathbf{X} {\boldsymbol \beta})^T(\mathbf{y}-\mathbf{X} {\boldsymbol \beta})\)?
  • What is \(\nabla_{\mathbf{\boldsymbol \beta}}\)?
  • What is \(\nabla_{\mathbf{\boldsymbol \beta}} \frac{1}{2} \left( \mathbf{y}^T\mathbf{y}-2{\boldsymbol \beta}^T \mathbf{X}^T\mathbf{y}+ {\boldsymbol \beta}^T \mathbf{X}^T \mathbf{X} {\boldsymbol \beta}\right) = \mathbf{X}^T \left(\mathbf{X} {\boldsymbol \beta} - \mathbf{y}\right)\)?

Gradient Descent in “Parameter” Space

  • initiallize gradient descent algorithm
Beta <- 0*Beta+2

alpha_learning_rate <- 0.01
step = 1
Betas_pars <- Beta %>% rename_at(step, 
                                 function(x) paste0(x, as.character(step), sep=''))
loss_pars <- c(sum((as.matrix(X)%*%as.matrix(Beta)-as.matrix(y))^2))
  • take some gradient descent steps
    • this is repeatedly manually run (10 times)
# https://en.wikipedia.org/wiki/Least_squares#Linear_least_squares
Beta <- Beta - alpha_learning_rate*t(as.matrix(X))%*%
  (as.matrix(X)%*%as.matrix(Beta)-as.matrix(y))
# https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/append
loss_pars <- append(loss_pars,
                    sum((as.matrix(X)%*%as.matrix(Beta)-as.matrix(y))^2))

step = step+1                        
Betas_pars %>% add_column(Beta) %>% 
  rename_at(step, 
            function(x) paste0(x,as.character(step), sep='')) -> Betas_pars

# https://tibble.tidyverse.org/reference/add_row.html
Betas_pars %>% add_row() -> tmp
tmp[3,] < -loss_pars
##   estimate1 estimate2 estimate3 estimate4 estimate5 estimate6 estimate7
## 3        NA        NA        NA        NA        NA        NA        NA
##   estimate8 estimate9 estimate10
## 3        NA        NA         NA
tmp %>% knitr::kable(digits=2) %>%
  kableExtra::kable_styling(full_width=FALSE)
estimate1 estimate2 estimate3 estimate4 estimate5 estimate6 estimate7 estimate8 estimate9 estimate10
2 1.55 1.23 1.01 0.86 0.75 0.67 0.62 0.58 0.56
2 1.51 1.26 1.13 1.07 1.03 1.02 1.01 1.00 1.00
NA NA NA NA NA NA NA NA NA NA
  • create a cost function visualization
# https://stackoverflow.com/questions/42158198/r-equivalent-of-pythons-np-dot-for-3d-array
# https://stackoverflow.com/questions/46843926/broadcasting-in-r/46845541#46845541
# https://stackoverflow.com/questions/37034623/simplest-way-to-repeat-matrix-along-3rd-dimension

two_var_cost_func <- function(x1, x2, y, par1.grid, par2.grid){
  n <- length(y)
  par1.x1 <- sweep(replicate(n, par1.grid), MARGIN=3, FUN='*', x1) 
  par2.x2 <- sweep(replicate(n, par2.grid), MARGIN=3, FUN='*', x2) 
  resid <- sweep(par1.x1+par2.x2, 3, y)
  SSE <- (resid^2)
  rowSums(SSE, dim=2)
}

range <- 500
Beta0_hat <- seq(-range,3*range,range/100)/range #seq(-33,33,length.out=300)#
Beta1_hat <- seq(-range,3*range,range/100)/range #seq(-100,100,length.out=300)#
Beta0_hat.grid <- outer(Beta0_hat, Beta1_hat, function(x1, x2) x1 )
Beta1_hat.grid <- outer(Beta0_hat, Beta1_hat, function(x1, x2) x2 )

SSE <- two_var_cost_func(x1=x*0+1, x2=x, y=y, 
                         par1.grid=Beta0_hat.grid, par2.grid=Beta1_hat.grid)

# https://plotly.com/r/figure-labels/
# https://github.com/plotly/plotly.js/issues/608
plot_ly(x=Beta0_hat.grid, y=Beta1_hat.grid, z=SSE, type='surface') %>%
  layout(title = '\n\nCost Function in\n"Model/Parameter Space"',
         scene = list(xaxis=list(title="β₀"),
                      yaxis=list(title = "β"),
                      zaxis=list(title = "SSE"))) -> cost_function

visualize the gradient descent path

# https://stackoverflow.com/questions/44619198/r-plot-ly-3d-graph-with-trace-line
# https://plotly.com/r/line-and-scatter/
cost_function %>%
  add_trace(x = unlist(c(Betas_pars[1,])), 
            y = unlist(c(Betas_pars[2,])),
            z = loss_pars, 
            type = "scatter3d",
            mode = "lines+markers",
            line = list(color = "red", width = 4)) -> parameter_space_plot
parameter_space_plot

Project the model space path into data space

(when run this live we can click through thumbnails of each of these figures)

  • PollEv.com/scottschwart658
  • Each point in parameter space corresponds to a model
  • So “Parameter” and “Model” space are equivalent
  • But models live in “Data” space, so we can project locations in “model/parameter” space into “data” space
set_names(1:10) %>% map(~ data_space_plot + 
                          geom_abline(aes(color='Model',
                                          intercept = Betas_pars[1,.x], 
                                          slope = Betas_pars[2,.x])) +
                           ggtitle('Viewing a point projection from "Model Space" into "Data Space"'))
## $`1`

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Gradient Descent Re-Imagined

  • Imagine each prediction as a parameter of the model…
  • We can then view “data” space as “parameter/model” space
    • the prediction value IS the parameter of the model
    • the model wants the prediction to be like th actual value
  • The gradient of the cost function then is the prediction relative to actual value
# http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
cbbPalette <- c("#009E73", "#CC79A7","#D55E00", "#000000",
                "#56B4E9", "#F0E442", "#0072B2", "#E69F00")
 
Beta <- 0*Beta-1

# https://www.r-graph-gallery.com/line-chart-several-groups-ggplot2.html
data_space_plot + geom_abline(aes(color='Model',
                                  intercept = Beta[1,], 
                                  slope = Beta[2,])) +
    geom_point(data=tibble(x=x, yhat=as.matrix(X)%*%as.matrix(Beta)),
               mapping=aes(x=x, y=yhat, color='Predictions')) +
    geom_line(data=tibble(x=rep(x,2),
                          group=x,
                          yhat=as.matrix(X)%*%as.matrix(Beta) %>% 
                               as.tibble() %>% 
                               bind_rows(tibble(estimate=y))), 
              mapping=aes(x=x, group=group, y=yhat$estimate, 
                          color='Gradients')) +
  ggtitle('Re-Imagining "Data Spece" as "Prediction Space"...
           ...so it\'s now a "Model" or "Parameter" space visualization')

Revised Gradient Descent Specification

  1. The prediction IS the model…
  2. The gradient of the cost function with respect to the model, then, is:

\[\LARGE \begin{align*} \sum_{i=1}^n (y_i - \hat y_i)^2 = {} & (\mathbf{y}-\mathbf{\mathbf{\hat y}})^T(\mathbf{y}-\mathbf{\mathbf{\hat y}})\\ = {} & \mathbf{y}^T\mathbf{y}-2\mathbf{y}^T \mathbf{\hat y}+\mathbf{\hat y}^T\mathbf{\hat y}\\ \nabla_{\mathbf{\hat y}} \frac{1}{2} (\mathbf{\hat y}-\mathbf{\hat y})^T(\mathbf{\hat y}-\mathbf{\hat y}) = {} & \nabla_{\mathbf{\hat y}} \frac{1}{2}(\mathbf{\hat y}^T\mathbf{\hat y} -2\mathbf{\hat y}^T \mathbf{ y}+ \mathbf{ y}^T\mathbf{ y}) \\ = {} & \frac{1}{2}(2 \mathbf{\hat y} -2\mathbf{ y}) = \mathbf{\hat y} - \mathbf{ y} \end{align*}\]

  1. To decrease the cost, go in the negative direction of the gradient, which points “up” relative to the cost function
  2. So if is larger than y, we should move in the negative direction
  • PollEv.com/scottschwart658
  • What is the parameter in \((\mathbf{y}-\mathbf{\mathbf{\hat y}})^T(\mathbf{y}-\mathbf{\mathbf{\hat y}})\)?
  • And it’s dimension?
  • What is \(\nabla_{\mathbf{\hat y}} \frac{1}{2} (\mathbf{\hat y}-\mathbf{\hat y})^T(\mathbf{\hat y}-\mathbf{\hat y}) = \mathbf{\hat y} - \mathbf{ y}\)?

Gradient Descent in “Prediction” Space

  • initiallize gradient descent algorithm
alpha_learning_rate <- 0.5
step = 1
Betas = Beta %>% rename_at(step, 
                           function(x) paste0(x, as.character(step), sep=''))
loss_pars <- c(sum((as.matrix(X)%*%as.matrix(Beta)-as.matrix(y))^2))
  • take some gradient descent steps
    • this is repeatedly manually run (10 times)
current_yhat <- as.matrix(X) %*% as.matrix(Beta)
new_yhat = current_yhat - alpha_learning_rate*(current_yhat-y)
Beta = lm(new_yhat~x) %>% broom::tidy() %>% select(estimate)
new_yhat = as.matrix(X) %*% as.matrix(Beta)

step = step+1                        
Betas %>% add_column(Beta) %>% 
  rename_at(step, 
            function(x) paste0(x, as.character(step), sep='')) -> Betas

loss_pars <- append(loss_pars,
                    sum((as.matrix(X)%*%as.matrix(Beta)-as.matrix(y))^2))

Betas %>% add_row() -> tmp
tmp[3,]<-loss_pars
tmp %>% knitr::kable(digits=2) %>%
  kableExtra::kable_styling(full_width=FALSE)
estimate1 estimate2 estimate3 estimate4 estimate5 estimate6 estimate7 estimate8 estimate9 estimate10
-1.00 -0.25 0.12 0.31 0.40 0.45 0.47 0.48 0.49 0.49
-1.00 0.00 0.50 0.75 0.87 0.94 0.97 0.98 0.99 0.99
265.15 67.58 18.19 5.84 2.75 1.98 1.79 1.74 1.72 1.72

Gradient Descent in “Prediction” Space

set_names(1:10) %>% map(~ data_space_plot + 
                          geom_abline(aes(color='Model',
                                          intercept=Betas[1,.x], 
                                          slope=Betas[2,.x])) +
                          geom_point(data=tibble(
                                          x=x, 
                                          yhat=as.matrix(X)%*%as.matrix(Betas[,.x])), 
                                     mapping=aes(x=x, y=yhat, 
                                                 color='Predictions')) +
                          geom_line(data=tibble(
                                         x=rep(x,2),
                                         group=x,
                                         yhat=as.matrix(X)%*%as.matrix(Betas[,.x]) %>% 
                                         as.tibble() %>% 
                                         bind_rows(tibble(V1=y))), 
                                    mapping=aes(x=x, group=group, 
                                                y=yhat$V1, 
                                                color='Gradients')) +
                          ggtitle('Path of Gradient Descent through "Prediction Space"'))
## $`1`

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## $`10`

Gradient Descent in “Parameter” Space

parameter_space_plot %>%
  add_trace(x = unlist(c(Betas[1,])), 
            y = unlist(c(Betas[2,])),
            z = loss_pars, 
            type = "scatter3d",
            mode = "lines+markers",
            line = list(color = "red", width = 4))

Gradient Boosting with Gradient Boosted Trees

  • If we’re just trying to make \(\hat y\) look like \(y\)
  • Then why not sequentially improve \(\hat y\) (towards \(y\))?
  • Enter Gradient Boosting…
    • which tries to follow the “gradients” in the “prediction space”
    • in order to “non-parametrically” improve \(\hat y\) (towards \(y\))
  • But what’s cool is
    • Gradient Boosting conceptualizes the gradient as a model fit instance
    • Prediction space IS a space of models
    • And now a step from one model to the next in this model space IS ALSO a model itself
      • I.e., think of adding vectors together to move through a space
  • Gradient boosting travels through model space by adding models together which move along the gradients of the cost function with respect to \(\hat y\)

\[ \LARGE \begin{align*} \mathbf{\hat y}_t = {}& \mathbf{\hat y}_{t-1}+\alpha (\mathbf{ y-\hat y}_{t-1})\\ \mathbf{\hat y}_t = {}& \mathbf{\hat y}_{t-1}+\alpha {\boldsymbol \epsilon}_{t-1}\\ \mathbf{\hat y}_t = {}& \mathbf{\hat y}_{t-1}+\alpha f({\boldsymbol \epsilon}_{t-1})\\ \mathbf{\hat y}_t = {}& \mathbf{\hat y}_{t-1}+\alpha \boldsymbol{\hat \epsilon}_{t-1} \end{align*} \] - \(\boldsymbol{\hat \epsilon}_{t-1}\) is a model - \(\mathbf{\hat y}_{t-1}\) is a model - \(\mathbf{\hat y}_{t} = \mathbf{\hat y}_{t-1}+\alpha \boldsymbol{\hat \epsilon}_{t-1}\) is another model - and \(\mathbf{\hat y}_{t}\) is closer to \(y\) than \(\mathbf{\hat y}_{t-1}\) was…

f0 <- function(x){
  0*x
}
support <- seq(min(x), max(x), .01)

step=1
fhat_model <- list(f0(support))
fhat_datas <- f0(x)
data_space_plot + geom_line(data=tibble(x=support, y=fhat_model[[step]], 
                                        color='GBT'),
                            mapping=aes(x=x,y=y,color=color))

y_leftover <- y
  • we can run this a few times to watch the model move in the directions dictated by the gradients
  • PollEv.com/scottschwart658
# https://www.statmethods.net/advstats/cart.html
# https://www.rdocumentation.org/packages/rpart/versions/4.1-15/topics/predict.rpart
alpha_learning_rate=0.1

y_leftover <- y_leftover-fhat_datas

stump <- rpart(y~x, data=tibble(x=x, y=y_leftover), 
               control=rpart.control(maxdepth=2, minbucket=1, minsplit=2, cp=0))
fhat_datas <- alpha_learning_rate*predict(stump) 
fhat_model <- append(fhat_model, 
                     list(fhat_model[[step]] + alpha_learning_rate *
                          as.numeric(predict(stump, newdata=tibble(x=support)))))
step = step+1

data_space_plot + geom_line(data=tibble(x=support, y=fhat_model[[step]], 
                                        color='GBT'),
                            mapping=aes(x=x,y=y,color=color))

data_space_plot + geom_line(data=tibble(x=support, y=fhat_model[[step]], 
                                        color='GBT'),
                            mapping=aes(x=x,y=y,color=color))

# https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/

# https://tidyr.tidyverse.org/reference/pivot_longer.html
animation <- fhat_model %>% 
  map(~ tibble(.x)) %>% 
  bind_cols() %>% 
  add_column(tibble(x=support)) %>% 
  pivot_longer(cols=starts_with(".x"))

# https://www.rdocumentation.org/packages/stringr/versions/1.4.0/topics/str_replace

animation %>% mutate(name=as.integer(str_replace(name,'.x...',''))) %>%
  ggplot(aes(x=x, y=value)) + geom_line() +
  transition_time(name) + labs(title = "Step: {frame_time}") +
  geom_point(data=tibble(x=x,y=y), mapping=aes(x,y))